"""simple docstring"""


import argparse
import re

import requests
import torch

# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import (
    BertTokenizer,
    BlipConfig,
    BlipForConditionalGeneration,
    BlipForImageTextRetrieval,
    BlipForQuestionAnswering,
)





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   Optional[int]:




						'''simple docstring'''



						lowerCamelCase__   ="https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"
						lowerCamelCase__   =Image.open(requests.get(__lowerCAmelCase  ,       stream=__lowerCAmelCase							).raw							).convert("RGB"							)

						lowerCamelCase__   =transforms.Compose(
						    [
						        transforms.Resize((image_size, image_size)  ,       interpolation=InterpolationMode.BICUBIC							),
						        transforms.ToTensor(),
						        transforms.Normalize((0.4814_5466, 0.457_8275, 0.4082_1073)  ,       (0.2686_2954, 0.2613_0258, 0.2757_7711)							),
						    ]							)
						lowerCamelCase__   =transform(__lowerCAmelCase							).unsqueeze(0							).to(__lowerCAmelCase							)
						return image





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   Optional[Any]:




						'''simple docstring'''



						if "visual_encoder" in key:
												lowerCamelCase__   =re.sub("visual_encoder*"  ,       "vision_model.encoder"  ,       __lowerCAmelCase							)
						if "blocks" in key:
												lowerCamelCase__   =re.sub(r"blocks"  ,       "layers"  ,       __lowerCAmelCase							)
						if "attn" in key:
												lowerCamelCase__   =re.sub(r"attn"  ,       "self_attn"  ,       __lowerCAmelCase							)
						if "norm1" in key:
												lowerCamelCase__   =re.sub(r"norm1"  ,       "layer_norm1"  ,       __lowerCAmelCase							)
						if "norm2" in key:
												lowerCamelCase__   =re.sub(r"norm2"  ,       "layer_norm2"  ,       __lowerCAmelCase							)
						if "encoder.norm" in key:
												lowerCamelCase__   =re.sub(r"encoder.norm"  ,       "post_layernorm"  ,       __lowerCAmelCase							)
						if "encoder.patch_embed.proj" in key:
												lowerCamelCase__   =re.sub(r"encoder.patch_embed.proj"  ,       "embeddings.patch_embedding"  ,       __lowerCAmelCase							)

						if "encoder.pos_embed" in key:
												lowerCamelCase__   =re.sub(r"encoder.pos_embed"  ,       "embeddings.position_embedding"  ,       __lowerCAmelCase							)
						if "encoder.cls_token" in key:
												lowerCamelCase__   =re.sub(r"encoder.cls_token"  ,       "embeddings.class_embedding"  ,       __lowerCAmelCase							)

						if "self_attn" in key:
												lowerCamelCase__   =re.sub(r"self_attn.proj"  ,       "self_attn.projection"  ,       __lowerCAmelCase							)

						return key





@torch.no_grad()
def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase=None							)				->   Optional[int]:




						'''simple docstring'''



						if config_path is not None:
												lowerCamelCase__   =BlipConfig.from_pretrained(__lowerCAmelCase							)
						else:
												lowerCamelCase__   =BlipConfig(projection_dim=512  ,       text_config={}  ,       vision_config={}							)

						lowerCamelCase__   =BlipForConditionalGeneration(__lowerCAmelCase							).eval()

						lowerCamelCase__   ="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth"

						lowerCamelCase__   =blip_decoder(pretrained=__lowerCAmelCase  ,       image_size=384  ,       vit="base"							)
						lowerCamelCase__   =pt_model.eval()

						lowerCamelCase__   =pt_model.state_dict()
						for key in modified_state_dict.copy():
												lowerCamelCase__   =modified_state_dict.pop(__lowerCAmelCase							)
												lowerCamelCase__   =rename_key(__lowerCAmelCase							)
												lowerCamelCase__   =value

						hf_model.load_state_dict(__lowerCAmelCase							)

						lowerCamelCase__   =384
						lowerCamelCase__   =load_demo_image(image_size=__lowerCAmelCase  ,       device="cpu"							)
						lowerCamelCase__   =BertTokenizer.from_pretrained("bert-base-uncased"							)
						lowerCamelCase__   =tokenizer(["a picture of"]							).input_ids

						lowerCamelCase__   =hf_model.generate(__lowerCAmelCase  ,       __lowerCAmelCase							)

						assert out[0].tolist() == [3_0522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

						lowerCamelCase__   =hf_model.generate(__lowerCAmelCase							)

						assert out[0].tolist() == [3_0522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

						if pytorch_dump_folder_path is not None:
												hf_model.save_pretrained(__lowerCAmelCase							)

						# model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
						lowerCamelCase__   =(
						    "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth"
						)

						lowerCamelCase__   =blip_vqa(pretrained=__lowerCAmelCase  ,       image_size=__lowerCAmelCase  ,       vit="base"							)
						vqa_model.eval()

						lowerCamelCase__   =vqa_model.state_dict()
						for key in modified_state_dict.copy():
												lowerCamelCase__   =modified_state_dict.pop(__lowerCAmelCase							)
												lowerCamelCase__   =rename_key(__lowerCAmelCase							)
												lowerCamelCase__   =value

						lowerCamelCase__   =BlipForQuestionAnswering(__lowerCAmelCase							)

						hf_vqa_model.load_state_dict(__lowerCAmelCase							)

						lowerCamelCase__   =["How many dogs are in this image?"]
						lowerCamelCase__   =tokenizer(__lowerCAmelCase  ,       return_tensors="pt"							).input_ids

						lowerCamelCase__   =hf_vqa_model.generate(__lowerCAmelCase  ,       __lowerCAmelCase							)
						print(tokenizer.decode(answer[0]							)							)

						assert tokenizer.decode(answer[0]							) == "[UNK] 1 [SEP]"
						if pytorch_dump_folder_path is not None:
												hf_vqa_model.save_pretrained(pytorch_dump_folder_path + "_vqa"							)

						lowerCamelCase__   ="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"

						lowerCamelCase__   =blip_itm(pretrained=__lowerCAmelCase  ,       image_size=__lowerCAmelCase  ,       vit="base"							)
						itm_model.eval()

						lowerCamelCase__   =itm_model.state_dict()
						for key in modified_state_dict.copy():
												lowerCamelCase__   =modified_state_dict.pop(__lowerCAmelCase							)
												lowerCamelCase__   =rename_key(__lowerCAmelCase							)
												lowerCamelCase__   =value

						lowerCamelCase__   =BlipForImageTextRetrieval(__lowerCAmelCase							)

						lowerCamelCase__   =["A picture of a woman with a dog sitting in a beach"]
						lowerCamelCase__   =tokenizer(
						    __lowerCAmelCase  ,       return_tensors="pt"  ,       padding="max_length"  ,       truncation=__lowerCAmelCase  ,       max_length=35  ,       ).input_ids

						hf_itm_model.load_state_dict(__lowerCAmelCase							)
						hf_itm_model.eval()

						lowerCamelCase__   =hf_itm_model(__lowerCAmelCase  ,       __lowerCAmelCase  ,       use_itm_head=__lowerCAmelCase							)
						lowerCamelCase__   =hf_itm_model(__lowerCAmelCase  ,       __lowerCAmelCase  ,       use_itm_head=__lowerCAmelCase							)

						assert out[0].item() == 0.2110_6874_9427_7954
						assert torch.nn.functional.softmax(out_itm[0]  ,       dim=1							)[:, 1].item() == 0.4_5698_8453_8650_5127

						if pytorch_dump_folder_path is not None:
												hf_itm_model.save_pretrained(pytorch_dump_folder_path + "_itm"							)


if __name__ == "__main__":
	a				=argparse.ArgumentParser()
	parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
	a				=parser.parse_args()

	convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)



"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


a				=logging.get_logger(__name__)

a				={
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}






class   __UpperCAmelCase		(      __lowerCAmelCase      ):
				A__ :  str    				=			'''deta'''
				A__ :  List[str]    				=			{
				    '''hidden_size''': '''d_model''',
				    '''num_attention_heads''': '''encoder_attention_heads''',
				}

				def __init__( self      ,					_lowerCamelCase=None      ,					_lowerCamelCase=900      ,					_lowerCamelCase=2048      ,					_lowerCamelCase=6      ,					_lowerCamelCase=2048      ,					_lowerCamelCase=8      ,					_lowerCamelCase=6      ,					_lowerCamelCase=1024      ,					_lowerCamelCase=8      ,					_lowerCamelCase=0.0      ,					_lowerCamelCase=True      ,					_lowerCamelCase="relu"      ,					_lowerCamelCase=256      ,					_lowerCamelCase=0.1      ,					_lowerCamelCase=0.0      ,					_lowerCamelCase=0.0      ,					_lowerCamelCase=0.0_2      ,					_lowerCamelCase=1.0      ,					_lowerCamelCase=True      ,					_lowerCamelCase=False      ,					_lowerCamelCase="sine"      ,					_lowerCamelCase=5      ,					_lowerCamelCase=4      ,					_lowerCamelCase=4      ,					_lowerCamelCase=True      ,					_lowerCamelCase=300      ,					_lowerCamelCase=True      ,					_lowerCamelCase=True      ,					_lowerCamelCase=1      ,					_lowerCamelCase=5      ,					_lowerCamelCase=2      ,					_lowerCamelCase=1      ,					_lowerCamelCase=1      ,					_lowerCamelCase=5      ,					_lowerCamelCase=2      ,					_lowerCamelCase=0.1      ,					_lowerCamelCase=0.2_5      ,					**_lowerCamelCase      ,					):
										if backbone_config is None:
																logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
																lowerCamelCase__   =CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"] )
										else:
																if isinstance(_lowerCamelCase      ,					_lowerCamelCase ):
																						lowerCamelCase__   =backbone_config.pop("model_type" )
																						lowerCamelCase__   =CONFIG_MAPPING[backbone_model_type]
																						lowerCamelCase__   =config_class.from_dict(_lowerCamelCase )

										lowerCamelCase__   =backbone_config
										lowerCamelCase__   =num_queries
										lowerCamelCase__   =max_position_embeddings
										lowerCamelCase__   =d_model
										lowerCamelCase__   =encoder_ffn_dim
										lowerCamelCase__   =encoder_layers
										lowerCamelCase__   =encoder_attention_heads
										lowerCamelCase__   =decoder_ffn_dim
										lowerCamelCase__   =decoder_layers
										lowerCamelCase__   =decoder_attention_heads
										lowerCamelCase__   =dropout
										lowerCamelCase__   =attention_dropout
										lowerCamelCase__   =activation_dropout
										lowerCamelCase__   =activation_function
										lowerCamelCase__   =init_std
										lowerCamelCase__   =init_xavier_std
										lowerCamelCase__   =encoder_layerdrop
										lowerCamelCase__   =auxiliary_loss
										lowerCamelCase__   =position_embedding_type
										# deformable attributes
										lowerCamelCase__   =num_feature_levels
										lowerCamelCase__   =encoder_n_points
										lowerCamelCase__   =decoder_n_points
										lowerCamelCase__   =two_stage
										lowerCamelCase__   =two_stage_num_proposals
										lowerCamelCase__   =with_box_refine
										lowerCamelCase__   =assign_first_stage
										if two_stage is True and with_box_refine is False:
																raise ValueError("If two_stage is True, with_box_refine must be True." )
										# Hungarian matcher
										lowerCamelCase__   =class_cost
										lowerCamelCase__   =bbox_cost
										lowerCamelCase__   =giou_cost
										# Loss coefficients
										lowerCamelCase__   =mask_loss_coefficient
										lowerCamelCase__   =dice_loss_coefficient
										lowerCamelCase__   =bbox_loss_coefficient
										lowerCamelCase__   =giou_loss_coefficient
										lowerCamelCase__   =eos_coefficient
										lowerCamelCase__   =focal_alpha
										super().__init__(is_encoder_decoder=_lowerCamelCase      ,					**_lowerCamelCase )

				@property
				def 						_a      ( self ):
										return self.encoder_attention_heads

				@property
				def 						_a      ( self ):
										return self.d_model






				def 						_a      ( self ):
										lowerCamelCase__   =copy.deepcopy(self.__dict__ )
										lowerCamelCase__   =self.backbone_config.to_dict()
										lowerCamelCase__   =self.__class__.model_type
										return output


